Many important protein families such as signaling receptors, ion channels, structural proteins and enzymes are embedded in the membrane of cells. Despite their importance, understanding of their structure, dynamics and interaction...
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Información proyecto MPFP
Líder del proyecto
UNIVERSITY OF WARWICK
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
309K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
Many important protein families such as signaling receptors, ion channels, structural proteins and enzymes are embedded in the membrane of cells. Despite their importance, understanding of their structure, dynamics and interactions lacks behind that of soluble proteins due to their hydrophobic nature which makes them difficult to study experimentally. We propose to recruit and retain in the Division of Metabolic and Vascular Health at the University of Warwick, Judith Klein-Seetharaman, membrane protein expert from the University of Pittsburgh, USA. In an interdisciplinary approach combining computational and experimental studies, she looks at these proteins from both, structural and systems biology point of views. She proposes to study membrane protein folding by characterizing denatured states of the model system and G protein coupled receptor mammalian rhodopsin in molecular detail by predicting these states and experimentally validating them with biophysical approaches such as 19F NMR spectroscopy. She will extend these studies to mutant rhodopsins that cause the retinal degeneration disease Retinitis pigmentosa and that are known to misfold and to other membrane proteins, in particular carnitine palmitoyltransferase 1A, of particular interest to our division. She will also investigate interactions of proteins with lipids using coarse grained simulations and in a systems biology approach interactions with other proteins to define the human membrane receptor interactome by establishing new collaborations within the division and with the Chemistry department. In this approach, computational machine learning methods are used to integrate large –omics databases to predict what interactions are likely for any given membrane receptor and then test the predictions using surface plasmon resonance and the TOXCAT assay. She proposes to disseminate her results in lecture series, conferences, a website, journal publications and through implementation of a graduate course.